Pemodelan Data Kemiskinan di Pulau Sumatera dengan Regresi Multilevel Spline Linear Truncated

Muhammad Ridzky Davala, Nurul Mutiara Annisa, Siswanto Siswanto, Anisa Kalondeng

Abstract

Poverty is one of the world's biggest challenges that is still a problem, both in developing and developed countries, including Indonesia. Around 27.5 million people live below the national poverty line in Indonesia. Because it is the largest archipelago, poverty problems in each region also vary, including on the Sumatra Island. One of the efforts to alleviate poverty can be done through identifying factors that affect the percentage of poor population using truncated linear spline multilevel regression model. Multilevel modeling is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of individual and group-level factors to the response variable. The predictor variables considered are per capita expenditure, open unemployment rate, and human development index at the district/city level (level-1), as well as population growth rate and economic growth rate at the provincial level (level-2). The results of this study show that the best multilevel regression model at level-1 uses three knot points, while at level-2 it uses two knot points. The factors that affect PPM in Sumatra Island in 2021 at level-1 are per capita expenditure and at level-2 are population growth rate and economic growth rate. The factors that affect percentage of poor population in Sumatra Island in 2021 are expected to provide a more in-depth view of the socio-economic conditions on the island of Sumatra.

Full Text:

PDF

References

World Bank, “Measuring Poverty,” https://www.worldbank.org/en/topic/measuringpoverty#1.

D. S. Sutopo and D. N. Shabrina, “Spektrum Perencanaan Pembangunan dalam Pengentasan Kemiskinan oleh Pemerintah Desa,” Jurnal Sosiologi Andalas, vol. 8(1), pp. 57–68, 2022.

L. Suryadi, “Kemiskinan dan Kerusakan Lingkungan,” https://bappeda.ntbprov.go.id/kemiskinan-dan-kerusakan-lingkungan/.

BPS, “Persentase Penduduk Miskin September 2020 Naik Menjadi 10,19 Persen,” https://www.bps.go.id/pressrelease/2021/02/15/1851/persentase-penduduk-miskin-september-2020-naik-menjadi-10-19-persen.html.

N. Handayani, “Pengaruh IPM, Pertumbuhan Ekonomi dan Pengangguran Terhadap Kemiskinan Kabupaten/Kota di Provinsi Jawa Tengah tahun 2017-2019,” Diponegoro Journal of Economics, vol. 11(1), pp. 26–36, 2023.

BPS Bengkulu, “Persentase Penduduk Miskin Menurut Provinsi di Sumatera (Persen), 2019-2021,” https://bengkulu.bps.go.id/indicator/23/304/1/persentase-penduduk-miskin-menurut-provinsi-di-sumatera.html.

M. G. Alfaiz, “Alfaiz, M. G. (2023). Analisis Faktor–Faktor Yang Mempengaruhi Kemiskinan di Pulau Sumatera (Periode 2014–2018),” Doctoral dissertation, Universitas Islam Indonesia, Yogyakarta, 2023.

A. R. Naila, “Pengaruh Pertumbuhan Ekonomi, Inflasi, dan Pengangguran terhadap Kemiskinan di Provinsi Lampung dalam Perspektif Ekonomi Islam Tahun 2011-2021,” Doctoral dissertation, UIN Raden Intan Lampung, Lampung, 2023.

J. J. Hox, Multilevel Analysis: Techniques and Applications Second Edition. Great Britain: Routledge, 2010.

N. Oskolkov, “Maximum Likelihood (ML) vs. REML,” https://towardsdatascience.com/maximum-likelihood-ml-vs-reml-78cf79bef2cf.

M. Masruroh and B. Matsaany, “Pemodelan Indeks Pembangunan Manusia di Provinsi Jawa Tengah Menggunakan Model Multilevel,” Perwira Journal of Science & Engineering, vol. 2(1), pp. 57–63, 2022.

V. A. Rastantra, “Analisis Multilevel Kemiskinan Rumah Tangga Pertanian di Provinsi Sulawesi Selatan Tahun 2020,” J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, vol. 14(2), pp. 93–98, 2022.

A. Purwanto and U. Aiman, “Analisis Regresi Linier Multilevel dengan Metode Restricted Estimation Maximum Likelihood (REML) untuk Data Pengukuran Berulang sebagai Kajian Model Pertumbuhan pada Kacang Tanah,” PLANTROPICA: Journal of Agricultural Science, vol. 7(1), pp. 40–47, 2022.

F. Rahim, I. N. Budiantara, and E. O. Permatasari, “Permodelan Regresi Nonparametrik Spline Truncated pada Data Angka Kematian Ibu di Jawa Timur,” Institut Teknologi Sepuluh Nopember, Surabaya, 2019.

M. H. Pratama, Sifriyani, and D. A. Nohe, “Regresi Nonparametrik Multivariabel dengan Pendekatan Spline Truncated pada Kasus Tuberculosis,” STATISTIKA Journal of Theoretical Statistics and Its Applications, vol. 22(1), pp. 87–93, 2022.

A. T. R. Dani, L. Ni’matuzzahroh, V. Ratnasari, and I. N. Budiantara, “Pemodelan Regresi Nonparametrik Spline Truncated pada Data Longitudinal,” Inferensi, vol. 4(1), pp. 47–55, 2021.

I. W. Rosanti and I. N. Budiantara, “Pemodelan Faktor-Faktor yang Mempengaruhi Morbiditas di Jawa Tengah Menggunakan Regresi Nonparametrik Spline Truncated,” Inferensi, vol. 3(2), pp. 107–114, 2020.

A. Anisa, A. Islamiyati, S. Sahriman, J. Massalesse, and B. Aprilia, “Model Regresi Kuantil Spline Orde Dua Dalam Menganalisis Perubahan Trombosit Pasien Demam Berdarah,” Jambura Journal of Mathematics, vol. 5(1), pp. 38–45, 2023.

D. Anastasia, Estimasi Parameter. Depok: Universitas Indonesia, 2008.

H. Goldstein, Multilevel Statistical Models 4nd edition, Second Edition. Routledge: Great Britain, 2011.

S. W. Raudenbush and A. S. Bryk, Hierarchical Linear Models: Applications and Data Analysis Methods. CA: Sage, 2002.

J. Suharianto and H. R. Lubis, “Pengaruh Pengangguran dan Inflasi terhadap Jumlah Penduduk Miskin di Provinsi Sumatera Utara,” Niagawan, vol. 11(2), pp. 168–177, 2022.

R. Putra and M. G. Fadhlurrahman, “Determination of the Best Knot and Bandwidth in Geographically Weighted Truncated Spline Nonparametric Regression Using Generalized Cross Validation,” MethodsX, vol. 10, pp. 1–17, 2023.

DOI: https://doi.org/10.13057/ijas.v7i1.80768

Refbacks

  • There are currently no refbacks.